30-second summary:
Predictive analytics is a statistical analysis technique that uses data mining and machine learning to predict future events. In the context of ecommerce, predictive analytics provides store owners with a deeper understanding of customer decisions and behaviors.
ML and AI tools are effective in combating the different types of ecommerce frauds. Smart data analytics and AI based systems can be used to analyze what a legit customer behavior looks like including false declines. A supervised decision tree is created to detect false transactions.
AI-powered product recommendation engines can suggest products based on the customers’ current preferences. Amazon says that its product recommendation engine drives 35% of its sales. The product recommendation engine suggests products related to the products that the customers have purchased in the past.
Dynamic pricing is a strategy that uses big data and AI to automatically add new pricing to products after carefully analyzing the current pricing trends and competitor prices. Offering competitive prices to the customers results in increased ecommerce revenue.
Intelligent ecommerce players never hold an inventory beyond its use. Smart AI solutions can be used to track the existing inventory, predict the market trends, and maintain a balance of demand and supply throughout the supply chain.
Artificial intelligence (AI) is the technology that will have the maximum impact on ecommerce in the coming years.
As per a report by IBM, over 90% of outperforming organizations are considering AI adoption at en enterprise-level. Besides, a report by Gartner predicts says that 37% of organizations have already implemented AI in some form.
From optimizing inventory levels to intelligent fraud management, AI is doing more than sending personalized product recommendations to customers.
Here are the top five ways how ecommerce businesses are using AI to scale up their profits:
1) Leveraging predictive analytics to improve the product offerings
Predictive analytics is a statistical analysis technique that uses data mining and machine learning to predict future events. In the context of ecommerce, predictive analytics provides store owners with a deeper understanding of customer decisions and behaviors.
It finds out different motivators in behaviors of target consumers so that ecommerce store owners can make use of the available data to improve their current product offerings.
Every customer interacts with an online store in a unique way. Predictive analytics helps to understand every variable in customer behavior and adapts the product offerings accordingly.
For example, products that are bought less by the customers can be replaced with products that are high in demand. Similarly, if the customer search data predicts that they are looking for new products every week, then the existing inventory can be replaced with newer products.
2) Applying advanced data analytics and machine learning for intelligent fraud management
Ecommerce sales attract 209% year-over-year revenue growth. With such exceptional performance in terms of growth, the ecommerce sector is susceptible to fraud. It is estimated that by the end of 2021, ecommerce companies will lose around $6.4 billion due to fraud.
Most of the frauds happen to be online. The different types of ecommerce fraud that are associated with ecommerce stores include:
Credit card fraud where hackers steal secret financial information of ecommerce companies to carry out fraudulent transactions.
Affiliate partner fraud where affiliate marketing partners charge commissions for sales that never happen.
Promo code abuse where fraudsters are able to get a discount multiple times using the same coupon code.
Shoe proxy frauds where fraudsters pose as different individuals to buy several pairs of limited edition shoes.
Security concerns while making a payment is one of the top reasons for shopping cart abandonment. ML and AI tools are effective in combating the different types of ecommerce frauds. Smart data analytics and AI based systems can be used to analyze what a legit customer behavior looks like including false declines. A supervised decision tree is created to detect false transactions.
For example, systems like Fraudlabs Pro have a central blacklist database containing millions of IP addresses and email addresses for smarter fraud detection. It screens every order for fraud patterns and even blocks multiple credit card attempts in a fraction of a second.
Accurate fraud detection for every single transaction helps ecommerce merchants reduce chargebacks and improve profits.
3) Creating algorithm-powered personalized product recommendations
Product recommendation engines are based on the historical approach and predictive approach. Historical approach algorithms will suggest products based on the decision that the customer took previously. However, the predictive approach algorithm will suggest products depending on what the customers could buy next.
For example, if a customer has bought a cricket bat in the past, the predictive algorithm will offer suggestions like batting gloves, sports shoes, leg guard, kitbag, armguard, etc.
Besides, AI-powered product recommendation engines can suggest products based on the customers’ current preferences. Amazon says that its product recommendation engine drives 35% of its sales. The product recommendation engine suggests products related to the products that the customers have purchased in the past.
But, AI recommendation engines have other uses as well. For example, if a cohort belonging to a country shops more for a particular product during festivals, then the AI engine can list that product on the homepage for people visiting from that region.
Similarly, if people of a certain age group are showing interest in buying a particular product, then the AI engine can list similar products to them.
Tools like Finteza use advanced ecommerce analytics to identify the products that customers have purchased in recent days and at what price. It also lets you know the most popular item among your customers. When you can detect the most popular products based on the customer location, you can offer those products to the customers to boost your sales.
4) Upgrading the site design and product prices after analyzing competitors
Dynamic pricing is the future of ecommerce. Major ecommerce players have already implemented robotic AI-based systems to offer dynamic pricing to the customers.
Dynamic pricing is a strategy that uses big data and AI to automatically add new pricing to products after carefully analyzing the current pricing trends and competitor prices. Offering competitive prices to the customers results in increased ecommerce revenue.
AI has the power to offer personalized prices to different customers. For example, if customers visit your competitor site and find that the product they wish to buy isn’t available. They will visit your site to buy that product.
Now, imagine if AI can provide this data to you and predict the chances of customers buying the product from your store. If the chances of buying are high, AI pricing algorithms will automatically increase the pricing of the product leading to higher profits.
Ecommerce giants like Amazon have a dynamic pricing strategy in place, and they change the prices of their products every ten minutes.
For example, tools like Minderest automatically check the inventory for different products in other ecommerce stores and dynamically raises the prices of products that are out of stock in other stores. It evaluates more than 20 KPIs and finds the best prices for your products.
5) Good inventory management and smart demand prediction
Inventory is a major part of an ecommerce store. Both overstocking and understocking are critical factors that directly affect the revenue generated from an online store.
Overstocking happens when companies stock products that have low demand and spend a huge amount only to manage the stock. Similarly, understocking happens when the companies do not have a stock of goods that are high in demand, and this results in losses.
Artificial intelligence helps ecommerce companies minimize losses while managing inventory. AI helps to forecast the demand for the products based on previous orders.
For example, AI can predict that the demand for certain products will rise in the coming months so that the seller can maintain an optimal stock level. This will minimize the losses from understocking.
Solutions like Qualetics are already leveraging AI to automate the management of optimal inventory levels. The system uses robots to check and refill the inventory after predicting the demand.
Intelligent ecommerce players never hold an inventory beyond its use. Smart AI solutions can be used to track the existing inventory, predict the market trends, and maintain a balance of demand and supply throughout the supply chain.
Conclusion
Delivering an amazing customer experience is the primary focus of every ecommerce company. AI is helping to reduce payment fraud, improve customer interactions across channels, offer personalized recommendations to the customers, and automate the existing customer acquisition processes. AI is the best weapon to combat with competitors and outperform them.
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